Effects of antipsychotics on bone mineral density and prolactin levels in patients with schizophrenia: a 12-month prospective study

Objective: Effects of conventional and atypical antipsychotics on bone mineral density (BMD) and serum prolactin levels (PRL) were examined in patients with schizophrenia.Methods: One hundred and sixty-three first-episode inpatients with schizophrenia were recruited, to whom one of three conventional antipsychotics (perphenazine, sulpiride, and chlorpromazine) or one of three atypical antipsychotics (clozapine, quetiapine, and aripiprazole)was prescribed for 12 months as appropriate. BMD and PRL were tested before and after treatment. Same measures were conducted in 90 matched healthy controls.Results Baseline BMD of postero-anterior L1–L4 range from 1.04 ± 0.17 to 1.42 ± 1.23, and there was no significant difference between the patients group and healthy control group. However, post-treatment BMD values in patients (ranging from 1.02 ± 0.15 to 1.23 ± 0.10) were significantly lower than that in healthy controls (ranging from 1.15 ± 0.12 to 1.42 ± 1.36). The BMD values after conventional antipsychotics were significantly lower than that after atypical antipsychotics. The PRL level after conventional antipsychotics (53.05 ± 30.25 ng/ml) was significantly higher than that after atypical antipsychotics (32.81 ± 17.42 ng/ml). Conditioned relevance analysis revealed significant negative correlations between the PRL level and the BMD values after conventional antipsychotics.Conclusion The increase of PRL might be an important risk factor leading to a high prevalence of osteoporosis in patients with schizophrenia on long-term conventional antipsychotic medication.<br/

Multiobjective Evolutionary Optimization for Prototype-Based Fuzzy Classifiers

Evolving intelligent systems (EISs), particularly, the zero-order ones have demonstrated strong performance on many real-world problems concerning data stream classification, while offering high model transparency and interpretability thanks to their prototype-based nature. Zero-order EISs typically learn prototypes by clustering streaming data online in a “one pass” manner for greater computation efficiency. However, such identified prototypes often lack optimality, resulting in less precise classification boundaries, thereby hindering the potential classification performance of the systems. To address this issue, a commonly adopted strategy is to minimise the training error of the models on historical training data or alternatively, to iteratively minimise the intra-cluster variance of the clusters obtained via online data partitioning. This recognises the fact that the ultimate classification performance of zero-order EISs is driven by the positions of prototypes in the data space. Yet, simply minimising the training error may potentially lead to overfitting, whilst minimising the intra-cluster variance does not necessarily ensure the optimised prototype-based models to attain improved classification outcomes. To achieve better classification performance whilst avoiding overfitting for zero-order EISs, this paper presents a novel multi-objective optimisation approach, enabling EISs to obtain optimal prototypes via involving these two disparate but complementary strategies simultaneously. Five decision-making schemes are introduced for selecting a suitable solution to deploy from the final non-dominated set of the resulting optimised models. Systematic experimental studies are carried out to demonstrate the effectiveness of the proposed optimisation approach in improving the classification performance of zero-order EISs

Multi-Objective Evolutionary Optimisation for Prototype-Based Fuzzy Classifiers

Evolving intelligent systems (EISs), particularly, the zero-order ones have demonstrated strong performance on many real-world problems concerning data stream classification, while offering high model transparency and interpretability thanks to their prototype-based nature. Zero-order EISs typically learn prototypes by clustering streaming data online in a “one pass” manner for greater computation efficiency. However, such identified prototypes often lack optimality, resulting in less precise classification boundaries, thereby hindering the potential classification performance of the systems. To address this issue, a commonly adopted strategy is to minimise the training error of the models on historical training data or alternatively, to iteratively minimise the intra-cluster variance of the clusters obtained via online data partitioning. This recognises the fact that the ultimate classification performance of zero-order EISs is driven by the positions of prototypes in the data space. Yet, simply minimising the training error may potentially lead to overfitting, whilst minimising the intra-cluster variance does not necessarily ensure the optimised prototype-based models to attain improved classification outcomes. To achieve better classification performance whilst avoiding overfitting for zero-order EISs, this paper presents a novel multi-objective optimisation approach, enabling EISs to obtain optimal prototypes via involving these two disparate but complementary strategies simultaneously. Five decision-making schemes are introduced for selecting a suitable solution to deploy from the final non-dominated set of the resulting optimised models. Systematic experimental studies are carried out to demonstrate the effectiveness of the proposed optimisation approach in improving the classification performance of zero-order EISs

Interval type-2 fuzzy multi-attribute decision-making approaches for evaluating the service quality of Chinese commercial banks

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In today’s world, with increased competition, the service quality of Chinese commercial banks is recognized as a major factor that is responsible for enhancing competitiveness. Therefore, it is necessary to evaluate and analyse the service quality of Chinese commercial banks to realize their stable development. The service quality evaluation could be recognized as a multi-attribute decision-making (MADM) problem with multiple assessment attributes, both being of a qualitative and quantitative nature. Owing to the growing complexity and high uncertainty of the financial environment, the assessments of attributes cannot always possibly express using a real and/or type-1 fuzzy number. Additionally, a heterogeneous relationship often exists among the attributes under many real decision cases. In this study, we create two MADM approaches to handle decision-making problems with interval type-2 fuzzy numbers (IT2FNs) and offer their application to service quality evaluations of commercial banks problems. Specifically, we first define some operations on IT2FNs based on Archimedean T-norms (ATs) and develop a bi-directional projection measure of IT2FNs. Next, by combining the generalized Banzhaf index, the Choquet integral and IT2FNs, we propose the interval type-2 fuzzy Archimedean Choquet (IT2FAC) operator, the Banzhaf IT2FAC (BIT2FAC) operator and the 2-additive BIT2FAC (2ABIT2FAC) operator. Then, we establish two optimal models for deriving the weights of attributes based on a bi-directional projection measure of IT2FNs and Banzhaf function. Finally, we create two novel MADM methods under interval type-2 fuzzy contexts, where an illustrative case concerning the service quality evaluation of Chinese commercial banks is used to explain the created MADM approaches

Bis(2-{2-[2-(benzyl­carbamo­yl)phen­oxy]acetamido}­eth­yl)ammonium nitrate ethanol disolvate

In the title compound, C36H40N5O6 +·NO3 −·2C2H5OH, the nitrate anion is disordered over the two orientations of equal occupancy while the solvent mol­ecule reveals large displacement parameters. The cation is formed by protonation of the N atom of a secondary amine in the middle of the flexible chain and the whole compound has crystallographically imposed C-2 symmetry with the crystallographic b axis. An O atom of the nitrate anion links the acidic H atoms of the cation via N—H⋯O hydrogen bonding. In addition, neighbouring cations are connected by inter­molecular N—H⋯O hydrogen bonds and π–π inter­actions between the benzamide groups of the cations [centroid–centroid distance = 4.000 (3) Å], forming a chain along [001]. The ethanol solvent mol­ecules are arranged on the side of the chain through O—H⋯O hydrogen bonds

Microfluidic mass production of stabilized and stealthy liquid metal nanoparticles

Functional nanoparticles comprised of liquid metals, such as eutectic gallium indium (EGaIn) and Galinstan, present exciting opportunities in the fields of flexible electronics, sensors, catalysts, and drug delivery systems. Methods used currently for producing liquid metal nanoparticles have significant disadvantages as they rely on both bulky and expensive high-power sonication probe systems, and also generally require the use of small molecules bearing thiol groups to stabilize the nanoparticles. Herein, we describe an innovative microfluidics-enabled platform as an inexpensive, easily accessible method for the on-chip mass production of EGaIn nanoparticles with tunable size distributions in an aqueous medium. We also report a novel nanoparticle-stabilization approach using brushed polyethylene glycol chains with trithiocarbonate end-groups negating the requirements for thiol additives whilst imparting a ‘stealth’ surface layer. Furthermore, we demonstrate a surface modification of the nanoparticles using galvanic replacement, and conjugation with antibodies. We envision that the demonstrated microfluidic technique can be used as an economic and versatile platform for the rapid production of liquid metal-based nanoparticles for a range of biomedical applications.

Interval Type-2 Fuzzy Programming Method for Risky Multicriteria Decision-Making with Heterogeneous Relationship

We propose a new interval type-2 fuzzy (IT2F) programming method for risky multicriteria decision-making (MCDM) problems with IT2F truth degrees, where the criteria exhibit a heterogeneous relationship and decision-makers behave according to bounded rationality. First, we develop a technique to calculate the Banzhaf-based overall perceived utility values of alternatives based on 2-additive fuzzy measures and regret theory. Subsequently, considering pairwise comparisons of alternatives with IT2F truth degrees, we define the Banzhaf-based IT2F risky consistency index (BIT2FRCI) and the Banzhaf-based IT2F risky inconsistency index (BIT2FRII). Next, to identify the optimal weights, an IT2F programming model is established based on the concept that BIT2FRII must be minimized and must not exceed the BIT2FRCI using a fixed IT2F set. Furthermore, we design an effective algorithm using an external archive-based constrained state transition algorithm to solve the established model. Accordingly, the ranking order of alternatives is derived using the Banzhaf-based overall perceived utility values. Experimental studies pertaining to investment selection problems demonstrate the state-of-the-art performance of the proposed method, that is, its strong capability in addressing risky MCDM problems